Abstract
Folksonomies have shown interesting potential for improving information discovery and exploration. Recent folksonomy systems explore the use of tag assignments, which combine Web resources with annotations (tags), and the users that have created the annotations. This article investigates on the effect of grouping resources in folksonomies, i.e. creating sets of resources, and using this additional structure for the tasks of search & ranking, and for tag recommendations. We propose several group-sensitive extensions of graph-based search and recommendation algorithms, and compare them with non group-sensitive versions. Our experiments show that the quality of search result ranking can be significantly improved by introducing and exploiting the grouping of resources (one-tailed t-Test, level of significance α=0.05). Furthermore, tag recommendations profit from the group context, and it is possible to make very good recommendations even for untagged resources– which currently known tag recommendation algorithms cannot fulfill.
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References
Abel, F., Frank, M., Henze, N., Krause, D., Plappert, D., and Siehndel, P. GroupMe!– Were Semantic Web meets Web 2.0. InInt. Semantic Web Conference (ISWC 2007) (November 2007).
Abel, F., Henze, N., and Krause, D. A Novel Approach to Social Tagging: GroupMe! In4th Int. Conf. on Web Information Systems and Technologies (WEBIST) (May 2008).
Bao, S., Xue, G., Wu, X., Yu, Y., Fei, B., and Su, Z. Optimizing Web Search using Social Annotations. InProc. of 16th Int. World Wide Web Conference (WWW ’07) (2007), ACM Press, pp.501–510.
Berners-Lee, T. Linked Data– Design Issues. Tech. Rep., W3C, May 2007.http://www.w3.org/DesignIssues/LinkedData.html.
Geisser, S. The Predictive Sample Reuse Method with Applications. InJournal of the American Statistical Association (June 1975), American Statistical Association, pp.320–328.
Halpin, H., Robu, V., and Shepherd, H. The Complex Dynamics of Collaborative Tagging. InProc. of 16th Int. World Wide Web Conference (WWW ’07) (New York, NY, USA, 2007), ACM Press, pp.211–220.
Haveliwala, T.H. Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search.IEEE Transactions on Knowledge and Data Engineering 15, 4 (2003), 784–796.
Hotho, A., Jäschke, R., Schmitz, C., and Stumme, G. BibSonomy: A Social Bookmark and Publication Sharing System. InProc. First Conceptual Structures Tool Interoperability Workshop (Aalborg, 2006), pp.87–102.
Hotho, A., Jäschke, R., Schmitz, C., and Stumme, G. FolkRank: A Ranking Algorithm for Folksonomies. InProc. of Workshop on Information Retrieval (FGIR) (Germany, 2006).
Jäschke, R., Marinho, L.B., Hotho, A., Schmidt-Thieme, L., and Stumme, G. Tag recommendations in folksonomies. InProc. 11th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD) (2007), pp.506–514.
Jeh, G., and Widom, J. SimRank: A Measure of Structural-Context Similarity. InProc. of Int. Conf. on Knowledge Discovery and Data Mining (SIGKDD) (Edmonton, Alberta, Canada, July 2002), ACM Press.
Li, X., Guo, L., and Zhao, Y.E. Tag-Based Social Interest Discovery. InProc. of the 17th Int. World Wide Web Conference (WWW’08) (New York, NY, USA, 2008), ACM Press, pp.675–684.
Marlow, C., Naaman, M., Boyd, D., and Davis, M. HT06, Tagging Paper, Taxonomy, Flickr, Academic Article, to read. InProc. of the 17th Conf. on Hypertext and Hypermedia (2006), ACM Press, pp.31–40.
Martens, H.A., and Dardenne, P. Validation and Verification of Regression in Small Data Sets. InChemometrics and Intelligent Laboratory Systems (December 1998), Elsevier, pp.99–121.
Vander Wal, T. Explaining and Showing Broad and Narrow Folksonomies.http://www.personalinfocloud.com/2005/02/explaining_and_html (February 2005).
Vander Wal, T. Folksonomy.http://vanderwal.net/folksono\-my.html (July 2007).
Mika, P. Ontologies are Us: A Unified Model of Social Networks and Semantics. InProc. Int. Semantic Web Conference (ISWC 2005) (November 2005), pp.522–536.
Page, L., Brin, S., Motwani, R., and Winograd, T. The PageRank Citation Ranking: Bringing Order to the Web. Tech. Rep., Stanford Digital Library Technologies Project, 1998.
Sigurbjörnsson, B., and van Zwol, R. Flickr Tag Recommendation Based on Collective Knowledge. InProc. of 17th Int. World Wide Web Conference (WWW ’08) (New York, NY, USA, 2008), ACM Press, pp.327–336.
Wu, X., Zhang, L., and Yu, Y. Exploring Social Annotations for the Semantic Web. InProc. of 15th Int. World Wide Web Conference (WWW ’06) (New York, NY, USA, 2006), ACM Press, pp.417–426.
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Abel, F., Henze, N., Krause, D., Kriesell, M. (2009). On the Effect of Group Structures on Ranking Strategies in Folksonomies. In: King, I., Baeza-Yates, R. (eds) Weaving Services and People on the World Wide Web. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00570-1_14
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DOI: https://doi.org/10.1007/978-3-642-00570-1_14
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